Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …

Recently, over-parameterized deep networks, with increasingly more network parameters
than training samples, have dominated the performances of modern machine learning …

Label smoothing is commonly used in training deep learning models, wherein one-hot
training labels are mixed with uniform label vectors. Empirically, smoothing has been shown …

Cross-entropy loss and focal loss are the most common choices when training deep neural
networks for classification problems. Generally speaking, however, a good loss function can …

Abstract The Randomized Response (RR) algorithm is a classical technique to improve
robustness in survey aggregation, and has been widely adopted in applications with …

Classical machine learning implicitly assumes that labels of the training data are sampled
from a clean distribution, which can be too restrictive for real-world scenarios. However …

Partial-label learning (PLL) is a multi-class classification problem, where each training
example is associated with a set of candidate labels. Even though many practical PLL …

Trained with the standard cross entropy loss, deep neural networks can achieve great
performance on correctly labeled data. However, if the training data is corrupted with label …

Supervised learning under label noise has seen numerous advances recently, while
existing theoretical findings and empirical results broadly build up on the class-conditional …